Legal claims defining the scope of protection. Each claim is shown in both the original legal language and a plain English translation.
1. A computer-implemented method of hyper-locating places-of-interest within a building structure, the method including: outlining a contour of the building structure based on geospatial coordinates of perimeter points of the building structure; receiving preliminary geospatial coordinates of a plurality of places-of-interest and qualifying, as contained, the places-of-interest that are within the contour; fitting one or more regression lines to the contained places-of-interest; generating adjusted places-of-interest by projecting the contained places-of-interest onto a nearest regression line; feeding the adjusted places-of-interest as input seeds to a Voronoi block generator to partition the building contour into blocks, each block associated with a block size; and storing respective centroids of the blocks as hyper-located geospatial coordinates of the contained places-of-interest.
This invention relates to indoor positioning and specifically addresses the problem of accurately hyper-locating points of interest within a building. The method begins by defining the boundary of a building structure using geospatial coordinates of its perimeter points. Then, preliminary geospatial coordinates for multiple places of interest within the building are received. These places of interest are then filtered to identify those that fall within the defined building contour, classifying them as "contained." To refine the location of these contained places of interest, one or more regression lines are fitted to their preliminary geospatial coordinates. The contained places of interest are then projected onto the nearest regression line, creating adjusted geospatial coordinates. These adjusted coordinates serve as input "seeds" for a Voronoi block generator. This generator partitions the building's contour into distinct blocks, with each block assigned a specific size. Finally, the centroid of each generated block is stored as the hyper-located geospatial coordinate for the corresponding contained place of interest. This process effectively refines the location of indoor points of interest.
2. The computer-implemented method of claim 1 , wherein the blocks are Voronoi cells.
This invention relates to a computer-implemented method for generating and analyzing spatial partitions, specifically using Voronoi cells. Voronoi cells are regions in a space where each point within a cell is closer to a designated seed point than to any other seed point. The method involves creating a partition of a spatial domain into these cells, where each cell corresponds to a distinct seed point. The method further includes determining the properties of these cells, such as their area, perimeter, or connectivity, and using these properties for applications like spatial indexing, data clustering, or terrain analysis. The method may also involve dynamically updating the Voronoi diagram in response to changes in the seed points or the spatial domain. This approach improves computational efficiency and accuracy in spatial partitioning tasks by leveraging the geometric properties of Voronoi cells, which naturally divide space into regions based on proximity. The method can be applied in fields such as computer graphics, geographic information systems, and robotics, where efficient spatial organization is critical.
3. The computer-implemented method of claim 1 , further including: assigning each of the adjusted places-of-interest to a corresponding block.
This invention relates to a computer-implemented method for organizing and managing places-of-interest (POIs) in a spatial or geographic context. The method addresses the challenge of efficiently grouping and categorizing POIs to improve data organization, retrieval, and analysis. The method involves adjusting the positions of POIs based on predefined criteria, such as spatial density, relevance, or user preferences, to optimize their distribution. These adjusted POIs are then assigned to corresponding blocks, which may represent geographic regions, grid cells, or other spatial divisions. The blocks serve as containers for the POIs, enabling structured storage, faster querying, and improved visualization. The method may also include additional steps such as filtering, clustering, or prioritizing POIs before assignment to blocks. This approach enhances the usability of POI data in applications like navigation systems, location-based services, and urban planning tools by providing a more organized and accessible structure. The method ensures that POIs are logically grouped, reducing redundancy and improving the efficiency of spatial data processing.
4. The computer-implemented method of claim 1 , further including: estimating principal axes of the building structure determined from principal component analysis the geospatial coordinates of the perimeter points and/or the contained places-of-interest; and dividing the contour into pieces and calculating a regression line for each of the pieces along the principal axes.
This invention relates to analyzing building structures using geospatial data to improve spatial modeling and representation. The method addresses challenges in accurately defining building contours and identifying key structural features from geospatial coordinates, which are often noisy or incomplete. The solution involves processing geospatial data to extract meaningful structural information for applications such as urban planning, navigation, or architectural analysis. The method begins by determining the perimeter of a building structure from geospatial coordinates of its boundary points and any contained places-of-interest, such as rooms or landmarks. Principal component analysis is then applied to these coordinates to estimate the principal axes of the building, which represent the dominant directions of structural alignment. The building contour is divided into segments, and a regression line is calculated for each segment along the principal axes. This step refines the contour by aligning it with the building's natural structural orientation, reducing errors caused by irregularities in the raw data. By combining principal component analysis with regression-based contour refinement, the method provides a more accurate and structured representation of building geometries. This approach enhances the reliability of spatial data used in mapping, simulation, and other geospatial applications. The technique is particularly useful for automated building modeling, where manual correction of noisy data is impractical.
5. The computer-implemented method of claim 1 , further including using spatial indices for qualifying, as contained, the places-of-interest that are within the contour, wherein the qualification is parameterized by a radius around the geospatial coordinates of the perimeter points, and wherein the radius is two meters.
This invention relates to geospatial data processing, specifically methods for identifying places-of-interest within a defined contour using spatial indexing. The problem addressed is efficiently determining which locations fall within a specified boundary, particularly in applications like mapping, navigation, or location-based services where precise spatial queries are needed. The method involves using spatial indices to qualify places-of-interest that lie within a contour. The qualification process is parameterized by a radius around the geospatial coordinates of the perimeter points defining the contour. This radius is set to two meters, ensuring that only locations within this buffer distance from the perimeter are considered. The spatial indices accelerate the search by organizing geospatial data in a way that allows quick filtering of relevant locations. The method may also include generating the contour from a set of perimeter points, where the contour is a closed shape formed by connecting these points. The spatial indices are used to efficiently query and filter places-of-interest based on their proximity to the contour, reducing computational overhead compared to brute-force distance calculations. This approach is particularly useful in scenarios requiring real-time or near-real-time geospatial analysis, such as dynamic boundary checks in navigation systems or location-based applications.
6. The computer-implemented method of claim 5 , further including qualifying at least some of the contained places-of-interest as select places-of-interests belonging to recognized brands, and adjusting block sizes for the select places-of-interests to known areas of location units of the recognized brands.
This invention relates to computer-implemented methods for processing and analyzing geographic data, particularly for identifying and categorizing places of interest within a geographic area. The method addresses the challenge of accurately mapping and distinguishing between different types of locations, especially those associated with recognized brands, to improve the precision of geographic data analysis. The method involves identifying places of interest within a geographic area and qualifying at least some of these places as select places of interest belonging to recognized brands. These select places are then adjusted to match known areas of location units associated with the recognized brands. For example, if a place of interest is identified as a fast-food restaurant belonging to a specific brand, the method adjusts the block size or boundary of that location to align with the standard footprint of that brand's restaurant units. This adjustment ensures that the geographic data accurately reflects the true dimensions and boundaries of the recognized brand's locations, improving the reliability of the data for applications such as mapping, navigation, and location-based services. The method may also include additional steps such as analyzing the geographic data to identify patterns or trends, generating reports or visualizations, or integrating the data with other datasets for further analysis. By refining the boundaries of recognized brand locations, the method enhances the accuracy and usability of geographic data for various applications.
7. The computer-implemented method of claim 1 , wherein each of the blocks is the same block size.
This invention relates to a computer-implemented method for processing data blocks in a storage system, addressing the problem of inefficient data handling due to variable block sizes, which can lead to wasted storage space, slower access times, and increased computational overhead. The method involves organizing data into fixed-size blocks, where each block maintains a uniform size throughout the system. This uniformity simplifies data management, reduces fragmentation, and improves performance by ensuring consistent storage and retrieval operations. The method may also include additional steps such as compressing the data within each block, encrypting the data, or distributing the blocks across multiple storage devices for redundancy or load balancing. By standardizing block sizes, the system achieves better alignment with hardware capabilities, such as disk sectors or memory pages, further optimizing performance. The fixed-size approach also facilitates easier data indexing, caching, and parallel processing, as the system can predict and manage block locations more efficiently. This method is particularly useful in large-scale storage systems, databases, or distributed computing environments where consistent block handling is critical for maintaining efficiency and reliability.
8. The computer-implemented method of claim 7 , wherein the block sizes have a minimum area of hundred square meters.
This invention relates to a computer-implemented method for optimizing block sizes in a spatial partitioning system, such as those used in geographic information systems (GIS) or autonomous vehicle navigation. The method addresses the problem of inefficient spatial partitioning, which can lead to excessive computational overhead or inaccurate data representation. The method involves dynamically adjusting block sizes based on spatial data characteristics to improve processing efficiency and accuracy. The method first partitions a spatial area into blocks, where each block represents a segment of the spatial data. The block sizes are determined based on predefined criteria, such as data density or resolution requirements. A key feature is that the block sizes have a minimum area of one hundred square meters, ensuring that the partitioning remains computationally feasible while maintaining sufficient granularity for accurate spatial analysis. The method may also include refining the block sizes iteratively to adapt to varying data distributions or environmental conditions. Additionally, the method may involve assigning unique identifiers to each block for efficient data retrieval and processing. The partitioning process may be applied recursively to further subdivide blocks if necessary, ensuring that the spatial data is represented with optimal precision. The method is particularly useful in applications requiring real-time spatial analysis, such as autonomous navigation, urban planning, or environmental monitoring. By enforcing a minimum block size, the method balances computational efficiency with spatial accuracy, reducing processing time while maintaining reliable data representation.
9. The computer-implemented method of claim 1 , wherein the outlining further includes sampling geospatial coordinates of additional perimeter points along distribution of the geospatial coordinates of the perimeter points with a step size, and wherein the step size of the sampling is twenty meters.
This invention relates to a computer-implemented method for processing geospatial data, specifically for outlining a perimeter defined by a set of geospatial coordinates. The method addresses the challenge of accurately and efficiently defining boundaries in geospatial applications, such as mapping, surveying, or geographic information systems (GIS), where precise perimeter delineation is critical. The method involves sampling additional perimeter points along the distribution of existing geospatial coordinates that define the perimeter. This sampling is performed with a fixed step size of twenty meters, ensuring consistent spacing between sampled points. By interpolating or extrapolating these additional points, the method refines the perimeter outline, improving accuracy and reducing gaps or irregularities in the boundary representation. This approach is particularly useful in applications requiring high-resolution boundary definitions, such as land surveying, urban planning, or environmental monitoring. The method may also include preprocessing steps to filter or smooth the original geospatial coordinates, ensuring that the sampled points are derived from a clean and representative dataset. The step size of twenty meters balances computational efficiency with sufficient resolution for most practical applications, though the method could be adapted for different step sizes depending on the required precision. The resulting refined perimeter can be used for further analysis, visualization, or integration into larger geospatial datasets.
10. The computer-implemented method of claim 1 , wherein the geospatial coordinates of a particular one of the centroids of a particular one of the blocks are determined based on a mean of geospatial coordinates of vertices of the particular one of the blocks.
This invention relates to geospatial data processing, specifically methods for determining centroids of geospatial blocks. The problem addressed is accurately calculating the central point (centroid) of irregularly shaped geospatial blocks, such as those in geographic information systems (GIS) or mapping applications, where precise centroid determination is critical for spatial analysis, routing, or data visualization. The method involves computing the centroid of a geospatial block by averaging the geospatial coordinates (e.g., latitude and longitude) of its vertices. For a given block, the coordinates of all its vertices are first identified. The centroid is then derived by calculating the arithmetic mean of these coordinates. This approach ensures that the centroid accurately represents the geometric center of the block, even if the block has an irregular shape. The method is particularly useful in applications requiring precise spatial referencing, such as urban planning, navigation systems, or environmental monitoring, where the centroid serves as a representative point for the entire block. By using vertex coordinates to determine the centroid, the method avoids complex geometric calculations and provides a computationally efficient solution. This technique is applicable to various geospatial datasets, including polygons representing administrative boundaries, land parcels, or natural features. The resulting centroid can be used for further spatial operations, such as distance calculations, clustering, or overlay analysis.
11. The computer-implemented method of claim 1 , further including selecting building structures for partitioning based on at least one of a minimum area of the building structures, geographic location of the building structures, elevation of the building structures, and a minimum number of the contained places-of-interest.
This invention relates to a computer-implemented method for partitioning building structures in a geographic information system (GIS) or mapping application. The method addresses the challenge of efficiently organizing and categorizing building structures to improve data management, visualization, and analysis in digital mapping systems. The primary goal is to enhance the usability and accuracy of geographic data by intelligently grouping or partitioning buildings based on specific criteria. The method involves selecting building structures for partitioning based on at least one of the following factors: a minimum area threshold, geographic location, elevation, or a minimum number of contained places-of-interest (POIs). By applying these criteria, the system can filter and group buildings in a way that optimizes spatial analysis, navigation, or urban planning applications. For example, buildings below a certain area may be excluded from partitioning, or structures in a specific geographic region may be prioritized. Elevation data can help distinguish multi-story buildings or terrain-based groupings, while POI density ensures that buildings with significant landmarks or points of interest are properly categorized. The method may also include additional steps such as analyzing the selected buildings to determine optimal partitioning boundaries, generating visual representations of the partitioned structures, or integrating the partitioned data into a larger mapping or GIS database. This approach improves the efficiency of geographic data processing and enhances the accuracy of location-based services.
12. The computer-implemented method of claim 1 , further including: receiving, from different data sources, multiple sets of geospatial coordinates of perimeter points for a particular building structure; and selecting at least one set from the multiple sets for outlining the contour of the particular building structure based on trustworthiness of the sources.
This invention relates to a computer-implemented method for accurately outlining the contour of a building structure using geospatial data from multiple sources. The problem addressed is the inconsistency and unreliability of geospatial data from different sources, which can lead to inaccurate building footprints or perimeters. The method involves receiving multiple sets of geospatial coordinates representing perimeter points of a building from different data sources. Each set of coordinates defines the outline of the building structure. The method then evaluates the trustworthiness of each data source and selects at least one set of coordinates based on this evaluation to determine the most accurate contour of the building. The selection process ensures that the chosen set of coordinates is derived from the most reliable source, improving the accuracy of the building's outline. This approach is particularly useful in applications such as urban planning, real estate, and geographic information systems (GIS), where precise building boundaries are essential. The method may also involve comparing the selected set of coordinates with other sets to refine the contour further, ensuring consistency and reliability in the final output.
13. The computer-implemented method of claim 1 , further including performing supplementary partitioning for certain ones of the contained places-of-interest.
This invention relates to computer-implemented methods for enhancing data partitioning in systems that manage places-of-interest (POIs), such as geographic locations, landmarks, or points of interest in a database. The method addresses the challenge of efficiently organizing and retrieving POI data by dynamically adjusting partitioning strategies to improve performance and accuracy. The method involves an initial partitioning process that categorizes POIs into distinct groups based on predefined criteria, such as geographic proximity, relevance, or user-defined attributes. To further refine this organization, the method performs supplementary partitioning for specific POIs that require additional granularity or specialized handling. This supplementary partitioning may involve subdividing existing groups, reclassifying POIs into more precise categories, or applying alternative partitioning algorithms tailored to the unique characteristics of the selected POIs. The supplementary partitioning step ensures that the system can adapt to varying data distributions and user needs, optimizing both storage efficiency and retrieval speed. By dynamically adjusting the partitioning structure, the method enhances the system's ability to handle large-scale POI datasets while maintaining high accuracy in search and recommendation tasks. This approach is particularly useful in applications like navigation systems, location-based services, and geographic information systems (GIS), where precise and efficient POI management is critical.
14. The computer-implemented method of claim 1 , wherein a count of the blocks is determined based on a count of the contained places-of-interest.
This invention relates to a computer-implemented method for optimizing data storage or processing in a system that handles places-of-interest, such as geographic locations, points of interest, or other relevant data points. The method involves organizing data into blocks, where each block contains a subset of the places-of-interest. The number of blocks is dynamically determined based on the total count of places-of-interest, ensuring efficient storage or processing by balancing the distribution of data across the blocks. This approach helps manage large datasets by preventing overcrowding in individual blocks while maintaining accessibility and performance. The method may be used in applications like mapping services, location-based analytics, or database management systems where efficient data organization is critical. By dynamically adjusting the block count, the system adapts to varying data volumes, improving scalability and resource utilization. The invention addresses the challenge of efficiently organizing and accessing large datasets of places-of-interest, ensuring optimal performance in data-intensive applications.
15. The computer-implemented method of claim 1 , further including buffering outlines of the contour inwards to ensure that the regression lines are within the contour.
This invention relates to computer-implemented methods for processing geometric data, specifically for ensuring that regression lines derived from contour data remain within the defined boundaries of the contour. The problem addressed is the potential for regression lines, which are used to approximate or analyze the shape of a contour, to extend beyond the original contour boundaries, leading to inaccuracies in analysis or visualization. The method involves buffering the outlines of the contour inwards. This buffering operation creates a modified contour that is slightly smaller than the original, ensuring that any regression lines calculated from this modified contour will lie entirely within the original contour boundaries. The buffering step adjusts the contour by a predetermined distance, effectively shrinking it to provide a safety margin. This ensures that the regression lines, which are derived from the buffered contour, do not extend beyond the original contour's edges, maintaining the integrity of the analysis. The method is particularly useful in applications where precise geometric accuracy is required, such as computer-aided design, medical imaging, or autonomous navigation, where deviations from the original contour could lead to errors in decision-making or further processing steps. By ensuring that regression lines remain within the contour, the method improves the reliability and accuracy of contour-based analyses.
16. The computer-implemented method of claim 1 , further including correlating the hyper-located geospatial coordinates of the contained places-of-interest with geospatial coordinates of visitor locations to detect visitor visits to the contained places-of-interest, to determine whether a mobile device was inside or outside of the building structure at a given timestamp, to determine whether a mobile device was inside or outside a particular contained places-of-interest at a given timestamp, to determine a duration for which a mobile device dwelled at a particular contained places-of-interest, and/or to determine that a mobile device visited a first building structure prior to visiting a second building structure.
This invention relates to a computer-implemented method for analyzing geospatial data to track visitor interactions with physical locations. The method addresses the challenge of accurately determining visitor behavior within and across building structures, including specific areas of interest within those structures. The method involves processing hyper-located geospatial coordinates of predefined places-of-interest contained within a building structure. These coordinates are correlated with geospatial coordinates of visitor locations, captured by mobile devices, to detect and analyze visitor visits. The system determines whether a mobile device was inside or outside the building structure at any given timestamp, as well as whether it was inside or outside a specific place-of-interest within the structure. Additionally, the method calculates the duration a mobile device dwelled at a particular place-of-interest and tracks sequential visits to multiple building structures, such as determining if a device visited a first building before a second. By leveraging precise geospatial data, the method enables detailed visitor behavior analysis, useful for applications like foot traffic monitoring, retail analytics, or security tracking. The system enhances accuracy in determining visitor presence and movement patterns within complex environments.
17. A computer-implemented method of hyper-locating places-of-interest within a building structure, the method including: outlining a contour of the building structure based on geospatial coordinates of perimeter points of the building structure; receiving preliminary geospatial coordinates of a plurality of places-of-interest and qualifying, as contained, the places-of-interest that are within the contour; sampling random points within the contour and iteratively projecting line segments through some of the random points to decompose the contour into disjoint maximum-area rectangles; determining a regression line for each of the rectangles; generating adjusted places-of-interest by projecting the contained places-of-interest onto a corresponding regression line; feeding the adjusted places-of-interest as input seeds to a Voronoi generator and partitioning the building contour into blocks; and storing respective centroids of the blocks as hyper-located geospatial coordinates of the contained places-of-interest.
This invention relates to a computer-implemented method for precisely locating places-of-interest within a building structure using geospatial data. The method addresses the challenge of accurately determining the positions of indoor locations, which can be difficult due to the lack of precise geospatial references inside buildings. The process begins by outlining the building's contour using geospatial coordinates of its perimeter points. Preliminary coordinates of multiple places-of-interest are then received, and only those locations confirmed to be within the building's contour are retained. The method then samples random points inside the contour and iteratively projects line segments through some of these points to divide the contour into the largest possible non-overlapping rectangles. For each rectangle, a regression line is calculated, and the retained places-of-interest are projected onto these lines to generate adjusted coordinates. These adjusted locations are then used as input seeds for a Voronoi diagram generator, which partitions the building's interior into distinct blocks. The centroids of these blocks are stored as the final, highly precise geospatial coordinates of the places-of-interest. This approach improves indoor location accuracy by leveraging geometric decomposition and spatial partitioning techniques.
18. The computer-implemented method of claim 17 , further including: determining whether a portion of the contour outlined by the iterative projection of the line segments is a rectangle based on fitting a bounding rectangle over the portion and calculating an area overlap between the bounding rectangle and the portion; and classifying the portion as the rectangle when the area overlap is equal to or above a threshold, and wherein the threshold is ninety-five percent.
This invention relates to computer vision techniques for detecting rectangular shapes in digital images. The problem addressed is accurately identifying rectangles in images where edges may be partially occluded or distorted, which can lead to false positives or missed detections in conventional methods. The method involves iteratively projecting line segments to outline contours in an image. A bounding rectangle is then fitted over a portion of the contour, and the area overlap between the bounding rectangle and the contour portion is calculated. If the overlap meets or exceeds a threshold of 95%, the portion is classified as a rectangle. This approach improves detection accuracy by ensuring that only well-fitted rectangles are identified, reducing errors from partial or irregular shapes. The method also includes determining whether a contour portion is a rectangle by evaluating the alignment of its edges. If the edges are parallel and perpendicular within a specified tolerance, the portion is classified as a rectangle. This step further refines detection by verifying geometric consistency. The combination of area overlap and edge alignment checks ensures robust rectangle detection in complex images. The technique is particularly useful in applications like document scanning, object recognition, and automated quality inspection where precise shape detection is critical.
19. The computer-implemented method of claim 18 , further including progressively increasing contour area enclosed by the portion until one of the disjoint maximum-area rectangles is outlined by the iterative projection.
This invention relates to computer-implemented methods for analyzing and processing geometric shapes, specifically focusing on identifying and outlining maximum-area rectangles within a given contour. The problem addressed is the efficient and accurate extraction of rectangular regions from complex shapes, which is useful in applications such as image processing, computer vision, and geometric analysis. The method involves iteratively projecting a portion of a contour to determine the maximum-area rectangle that can be enclosed within it. The process begins by defining an initial contour and progressively increasing the area enclosed by the portion of the contour until one of the disjoint maximum-area rectangles is outlined. This iterative projection ensures that the largest possible rectangle is identified while maintaining the integrity of the contour's structure. The method may also include steps to refine the contour or adjust the projection parameters to improve accuracy. The technique is particularly valuable in scenarios where precise geometric analysis is required, such as in automated design, pattern recognition, or quality control systems. By systematically expanding the contour area and evaluating the resulting projections, the method ensures that the optimal rectangle is identified without manual intervention. This approach enhances computational efficiency and reduces errors compared to traditional methods that rely on fixed thresholds or heuristic-based approaches.
20. The computer-implemented method of claim 19 , further including terminating the iterative projection when the contour area cumulatively enclosed by the disjoint maximum-area rectangles exceeds a threshold, and wherein the threshold is ninety percent.
This invention relates to a computer-implemented method for optimizing the coverage of a contour by iteratively projecting disjoint maximum-area rectangles. The method addresses the challenge of efficiently approximating complex shapes or regions with a set of rectangular segments, which is useful in applications like computer vision, image processing, and geometric analysis. The process involves iteratively selecting the largest possible rectangle that fits within the remaining contour area, subtracting this rectangle from the contour, and repeating the process. The iteration continues until the cumulative area of the selected rectangles reaches a predefined threshold, specifically ninety percent of the total contour area. This ensures a balance between computational efficiency and coverage accuracy. The method may also include additional steps such as refining the contour boundaries or adjusting the rectangle selection criteria to improve the approximation. The approach is particularly valuable in scenarios where rectangular decomposition is required for further analysis, such as object recognition, shape matching, or spatial indexing. By terminating the process at a high coverage threshold, the method ensures that the resulting set of rectangles provides a sufficiently accurate representation of the original contour while minimizing computational overhead.
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April 14, 2020
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